Survival Analysis
This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.
Overview
This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data. General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates. The course will also require participants to use a convenient statistical package (e.g., SAS, JMP, STATA, R, or S+) to analyze survival analysis data.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
After completing this course you will be able to describe survival data and format it appropriately for analysis and understanding. You will learn to graph data, specify and fit proportional hazard models, check assumptions and compute hazard ratios. You will understand stratified and fully-extended PH models and how they are applied to real-world datasets.
- Describe survival data, and the roles played by censoring, and survival and hazard functions
- Format data appropriately for analysis, and understanding
- Graph survival data, and the Kaplan – Meier curve
- Specify and fit the Cox Proportional Hazards model
- Check the PH assumption, and compute the hazard ratio
- Add stratification to specify a Stratified Cox model (with and without interaction)
- Describe the fully-extended Cox model
- Apply models to the “Addicts” data, and the Stanford Heart Transplant Study
Who Should Take This Course
Investigators designing, conducting or analyzing medical studies or clinical trials. Researchers in any field (including engineering) working with data on how long things last.
Our Instructors
Course Syllabus
Week 1
Overview
- An overview of survival analysis methods
- Censoring
- Key terms: survival and hazard functions
- Goals of a survival analysis
- Data layout for the computer
- Data layout for understanding
- Descriptive statistics for survival analysis- the hazard ratio
- Graphing survival data- Kaplan Meier
- The Log Rank and related tests.
Week 2
Introduction to Proportional Hazards Models
- Introduction to the Cox Proportional Hazards (PH) model- computer example
- Model definition and features
- Maximum likelihood estimation for the Cox PH model
- Computing the hazard ratio in the Cox PH model
- The PH assumption
- Adjusted survival curves
- Checking the proportional hazard assumption
- The likelihood function for the Cox PH model
Week 3
The Stratified Cox Model
- Introduction to the Stratified Cox procedure
- The no-interaction Stratified Cox model
- The Stratified Cox model that allows for interaction
Week 4
Definitions and Examples
- Definition and examples of time-dependent variables
- Definition and features of the extended Cox model
- Stanford Heart Transplant Study Example
- Addicts Dataset Example
- The likelihood function for the extended Cox model.
Class Dates
2024
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
2025
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
Prerequisites
Course participants should have some experience with computer procedures for regression modeling.
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Analysis of Survey Data from Complex Sample Designs
Additional Information
Homework
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.
In addition to assigned readings, this course also has supplemental readings available online.
Course Text
The required text is Survival Analysis- A Self Learning Text, 3rd edition by David G Kleinbaum and Mitchel Klein. It may be purchased here.
Software
Course Fee & Information
Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
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